
antoun2020arabert. The authors fine-tune two versions of AraBERT: AraBERTv0.1, which employs no Arabic segmentation nor pre-possessing, and AraBERTv1, which is based on segmentation nor pre-possessing. They fine-tune these two versions on 5 sentiment datasets. These are HARD [1]}, the balanced data for ASTD (which we will refer to as ASTD-B) [2]}, ArSenTD-Lev [3]}, AJGT [4]}, and the unbalanced positive and negative classes for LABR [5]}. antoun2020arabert split the data into 80/20 for training/test, respectively and report results in accuracy using the best epoch identified on test data. For a valid comparison, we use the same data splits and evaluation set up as [6]}. Their best results are obtained by fine-tuning AraBERTv1.
[mam]Do we use their splits?

obeid2020camel. The authors fine-tune mBERT and AraBERT on the merged CAMelsent datasets and compared their results with Mazajak [7]}. obeid2020camel use \(F_1\)PN score for evaluation, which was defined by SemEval-2017 and also used by Majazak [7]}. \(F_1\)PN is the macro \(F_1\)  score over the positive and negative classes only, while neglecting the neutral class.

mageed-2020-aranet. The authors fine-tune mBERT on the AraNETSent data and report results in \(F_1\)  score on test data.

